Genetic Algorithm

When we agreed to edit this book for a second edition, we looked forward to
a bit of updating and including some of our latest research results. However,
the effort grew rapidly beyond our original vision. The use of genetic algorithms
(GAs) is a quickly evolving field of research, and there is much new to
recommend. Practitioners are constantly dreaming up new ways to improve
and use GAs. Therefore this book differs greatly from the first edition.

Automated summarization methods can be deﬁned as “language-independent,” if they are not based on any languagespeciﬁc knowledge. Such methods can be used for multilingual summarization deﬁned by Mani (2001) as “processing several languages, with summary in the same language as input.” In this paper, we introduce MUSE, a languageindependent approach for extractive summarization based on the linear optimization of several sentence ranking measures using a genetic algorithm.

This paper proposes a novel approach to the induction of Combinatory Categorial Grammars (CCGs) by their potential afﬁnity with the Genetic Algorithms (GAs). Speciﬁcally, CCGs utilize a rich yet compact notation for lexical categories, which combine with relatively few grammatical rules, presumed universal. Thus, the search for a CCG consists in large part in a search for the appropriate categories for the data-set’s lexical items. We present and evaluates a system utilizing a simple GA to successively search and improve on such assignments. ...

These pages introduce some fundamentals of genetics
algorithms. Pages are intended to be used for learning
about genetics algorithms without any previous
knowledge from this area. Only some knowledge of
computer programming is assumed. You can find here
several interactive Java applets demonstrating work of
genetic algorithms.

Science arises from the very human desire to understand and control the world. Over the course of history, we
humans have gradually built up a grand edifice of knowledge that enables us to predict, to varying extents, the
weather, the motions of the planets, solar and lunar eclipses, the courses of diseases, the rise and fall of
economic growth, the stages of language development in children, and a vast panorama of other natural,
social, and cultural phenomena. More recently we have even come to understand some fundamental limits to
our abilities to predict.

Genetic Algorithms (GAs) are global optimization techniques used in many real-life
applications. They are one of several techniques in the family of Evolutionary
Algorithms – algorithms that search for solutions to optimization problems by
“evolving” better and better solutions.
A Genetic Algorithm starts with a population of possible solutions for the desired
application. The best ones are selected to become parents and then, using genetic
operators like crossover and mutation, offspring are generated....

This paper presents a genetic algorithm based approach to the automatic discovery of finitestate a u t o m a t a (FSAs) from positive data. FSAs are commonly used in computational phonology, but - given the limited learnability of FSAs from arbitrary language subsets - are usually constructed manually. The approach presented here offers a practical automatic method that helps reduce the cost of manual FSA construction.